Research on traffic sign recognition method based on multi-scale convolution neural network

Tiancheng Wei; Xiaofeng Chen; Yuanliang Yin

doi:10.1051/jnwpu/20213940891

All issues

Volume 39 / No 4 (August 2021)

JNWPU, 39 4 (2021) 891-900

References

Open Access

Issue		JNWPU Volume 39, Number 4, August 2021


Page(s)		891 - 900
DOI		https://doi.org/10.1051/jnwpu/20213940891
Published online		23 September 2021

Zhang J, Wang F, Wang K, et al. Data-driven intelligent transportation systems: a survey[J]. IEEE Trans on Intelligent Transpor-tation Systems, 2011, 12(4): 1624–1639 [Article] [Google Scholar]
Seo Y, Lee J, Zhang W, et al. Recognition of highway workzones for reliable autonomous driving[J]. IEEE Trans on Intelligent Transportation Systems, 2015, 16(2): 708–718 [Article] [Google Scholar]
Fan Yanjun, Zhang Weigong. Traffic sign detection and classification for advanced driver assistant systems[C]//International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), Zhangjiajie, 2015: 1335–1339 [Google Scholar]
Ge Xia, Yu Fengqin, Chen Ying. Traffic sign recognition based on blocking self-adaptation fusion features[J]. Computer Engineering and Applications, 2017, 53(3): 188–192 [Article] (in Chinese) [Google Scholar]
Sha Sha, Xiao Xuegang. Traffic sign detection method with multi-channel integration[J]. Computer Engineering, 2009, 35(6): 202–204 [Article] (in Chinese) [Google Scholar]
Madani A, Yusof R. Traffic sign recognition based on color, shape, and pictogram classification using support vector machines[J]. Neural Computing & Applications, 2017, 36(5): 1–11 [Article] [Google Scholar]
Aziz S, Mohamed E A, Youssef F. Traffic sign recognition based on multi-feature fusion and ELM classifier[J]. Procedia Computer Science, 2018, 127(1): 146–153 [Article] [Google Scholar]
Boumediene M, Cudel C, Basset M, et al. Triangular traffic signs detection based on RSLD algorithm[J]. Machine Vision and Applications, 2013, 24(8): 1721–1732 [Article] [Google Scholar]
Lillo-Castellano J M, Jiménez I, Pozuelo C, et al. Traffic sign segmentation and classify-cation using statistical learning methods[J]. Neurocomputing, 2015, 153: 286–299 [Article] [CrossRef] [Google Scholar]
Girshick R. Fast R-CNN[J/OL]. (2015-09-27)[2020-09-01]. https://arxiv.org/pdf/1504.08083.pdf [Google Scholar]
Cheng P, Liu W, Zhang Y, et al. LOCO: local context based faster R-CNN for small traffic sign detection[C]//International Conference on Multimedia Modeling, Cham, 2018 [Google Scholar]
Xiong C, Cong W, Ma W, et al. A traffic sign detection algorithm based on deep convolutional neural network[C]//IEEE International Conference on Signal and Image Processing, 2016 [Google Scholar]
Ren Shaoqing, He Kaiming, Girshick Ross, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 [Article] [Google Scholar]
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans on Patcern Analysis and Machrne Interlligence, 2015, 39(4): 640–651 [Article] [Google Scholar]
Li Kai, Han Bing, Zhang Jingtao. Traffic sign detection and recognition based on oonditional random field and multi-scale convolutional neural network[J]. Journal of Computer Applications, 2018, 38(suppl 2): 270–275 [Article] (in Chinese) [Google Scholar]
Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]//The 2013 International Joint Conference on Neural Networks, Dallas, TX, 2013: 1–8 [Google Scholar]
Zhu Z, Liang D, Zhang S, et al. Traffic-sign detection and classification in the wild[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Zhang J, Wang F, Wang K, et al. Data-driven intelligent transportation systems: a survey[J]. IEEE Trans on Intelligent Transpor-tation Systems, 2011, 12(4): 1624–1639 [Article] [Google Scholar]

[2] Seo Y, Lee J, Zhang W, et al. Recognition of highway workzones for reliable autonomous driving[J]. IEEE Trans on Intelligent Transportation Systems, 2015, 16(2): 708–718 [Article] [Google Scholar]

[3] Fan Yanjun, Zhang Weigong. Traffic sign detection and classification for advanced driver assistant systems[C]//International Conference on Fuzzy Systems and Knowledge Discovery(FSKD), Zhangjiajie, 2015: 1335–1339 [Google Scholar]

[4] Ge Xia, Yu Fengqin, Chen Ying. Traffic sign recognition based on blocking self-adaptation fusion features[J]. Computer Engineering and Applications, 2017, 53(3): 188–192 [Article] (in Chinese) [Google Scholar]

[5] Sha Sha, Xiao Xuegang. Traffic sign detection method with multi-channel integration[J]. Computer Engineering, 2009, 35(6): 202–204 [Article] (in Chinese) [Google Scholar]

[6] Madani A, Yusof R. Traffic sign recognition based on color, shape, and pictogram classification using support vector machines[J]. Neural Computing & Applications, 2017, 36(5): 1–11 [Article] [Google Scholar]

[7] Aziz S, Mohamed E A, Youssef F. Traffic sign recognition based on multi-feature fusion and ELM classifier[J]. Procedia Computer Science, 2018, 127(1): 146–153 [Article] [Google Scholar]

[8] Boumediene M, Cudel C, Basset M, et al. Triangular traffic signs detection based on RSLD algorithm[J]. Machine Vision and Applications, 2013, 24(8): 1721–1732 [Article] [Google Scholar]

[9] Lillo-Castellano J M, Jiménez I, Pozuelo C, et al. Traffic sign segmentation and classify-cation using statistical learning methods[J]. Neurocomputing, 2015, 153: 286–299 [Article] [CrossRef] [Google Scholar]

[10] Girshick R. Fast R-CNN[J/OL]. (2015-09-27)[2020-09-01]. https://arxiv.org/pdf/1504.08083.pdf [Google Scholar]

[11] Cheng P, Liu W, Zhang Y, et al. LOCO: local context based faster R-CNN for small traffic sign detection[C]//International Conference on Multimedia Modeling, Cham, 2018 [Google Scholar]

[12] Xiong C, Cong W, Ma W, et al. A traffic sign detection algorithm based on deep convolutional neural network[C]//IEEE International Conference on Signal and Image Processing, 2016 [Google Scholar]

[13] Ren Shaoqing, He Kaiming, Girshick Ross, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 [Article] [Google Scholar]

[14] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans on Patcern Analysis and Machrne Interlligence, 2015, 39(4): 640–651 [Article] [Google Scholar]

[15] Li Kai, Han Bing, Zhang Jingtao. Traffic sign detection and recognition based on oonditional random field and multi-scale convolutional neural network[J]. Journal of Computer Applications, 2018, 38(suppl 2): 270–275 [Article] (in Chinese) [Google Scholar]

[16] Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]//The 2013 International Joint Conference on Neural Networks, Dallas, TX, 2013: 1–8 [Google Scholar]

[17] Zhu Z, Liang D, Zhang S, et al. Traffic-sign detection and classification in the wild[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016 [Google Scholar]